


"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowerCAmelCase							:    Optional[Any]					   =	logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='cifar10'     ,	metadata={'help': 'Name of a dataset from the datasets package'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'The column name of the images in the files.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'A folder containing the training data.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'A folder containing the validation data.'} )
  lowerCAmelCase__      			=	field(
      default=0.15     ,	metadata={'help': 'Percent to split off of train for validation.'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of training examples to this '
              'value if set.'
          )
      }     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
              'value if set.'
          )
      }     ,	)


  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     {}
     if self.train_dir is not None:
        _lowerCamelCase   :				Any							=     self.train_dir
     if self.validation_dir is not None:
        _lowerCamelCase   :				Tuple							=     self.validation_dir
     _lowerCamelCase   :				Tuple							=     data_files if data_files else None




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={
          'help': (
              'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
          )
      }     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={
          'help': (
              'Override some existing default config settings when a model is trained from scratch. Example: '
              'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
          )
      }     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
  lowerCAmelCase__      			=	field(
      default='main'     ,	metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,	)
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Name or path of preprocessor config.'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={
          'help': (
              'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
              'with private models).'
          )
      }     ,	)
  lowerCAmelCase__      			=	field(
      default=0.75     ,	metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )




@dataclass
class 							A_      (       _a ):
  lowerCAmelCase__      			=	field(
      default=1E-3     ,	metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Optional[int]:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     torch.stack([example["pixel_values"] for example in examples]       )
   return {"pixel_values": pixel_values}







def        lowerCamelCase_(		)    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)       )
   if len(sys.argv       ) == 2 and sys.argv[1].endswith(".json"       ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      _lowerCamelCase   :				Optional[int]							=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
   else:
      _lowerCamelCase   :				Optional[Any]							=     parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry("run_mae"			,		_lowerCamelCase			,		_lowerCamelCase       )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,		datefmt="%m/%d/%Y %H:%M:%S"			,		handlers=[logging.StreamHandler(sys.stdout       )]			,		)

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   _lowerCamelCase   :				Optional[Any]							=     training_args.get_process_log_level()
   logger.setLevel(_lowerCamelCase       )
   transformers.utils.logging.set_verbosity(_lowerCamelCase       )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + F"""distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}"""       )
   logger.info(F"""Training/evaluation parameters {training_args}"""       )

   # Detecting last checkpoint.
   _lowerCamelCase   :				Optional[Any]							=     None
   if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
      _lowerCamelCase   :				Tuple							=     get_last_checkpoint(training_args.output_dir       )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
         raise ValueError(
             F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             "Use --overwrite_output_dir to overcome."       )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."       )

    # Initialize our dataset.
   _lowerCamelCase   :				List[str]							=     load_dataset(
       data_args.dataset_name			,		data_args.dataset_config_name			,		data_files=data_args.data_files			,		cache_dir=model_args.cache_dir			,		use_auth_token=True if model_args.use_auth_token else None			,		)

   # If we don't have a validation split, split off a percentage of train as validation.
   _lowerCamelCase   :				Dict							=     None if '''validation''' in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split			,		_lowerCamelCase       ) and data_args.train_val_split > 0.0:
      _lowerCamelCase   :				Optional[Any]							=     ds['''train'''].train_test_split(data_args.train_val_split       )
      _lowerCamelCase   :				Tuple							=     split['''train''']
      _lowerCamelCase   :				Tuple							=     split['''test''']

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   _lowerCamelCase   :				int							=     {
       '''cache_dir''': model_args.cache_dir,
       '''revision''': model_args.model_revision,
       '''use_auth_token''': True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      _lowerCamelCase   :				Dict							=     ViTMAEConfig.from_pretrained(model_args.config_name			,		**_lowerCamelCase       )
   elif model_args.model_name_or_path:
      _lowerCamelCase   :				Optional[int]							=     ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,		**_lowerCamelCase       )
   else:
      _lowerCamelCase   :				Union[str, Any]							=     ViTMAEConfig()
      logger.warning("You are instantiating a new config instance from scratch."       )
      if model_args.config_overrides is not None:
         logger.info(F"""Overriding config: {model_args.config_overrides}"""       )
         config.update_from_string(model_args.config_overrides       )
         logger.info(F"""New config: {config}"""       )

    # adapt config
   config.update(
       {
           "mask_ratio": model_args.mask_ratio,
           "norm_pix_loss": model_args.norm_pix_loss,
       }       )

   # create image processor
   if model_args.image_processor_name:
      _lowerCamelCase   :				Optional[int]							=     ViTImageProcessor.from_pretrained(model_args.image_processor_name			,		**_lowerCamelCase       )
   elif model_args.model_name_or_path:
      _lowerCamelCase   :				str							=     ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,		**_lowerCamelCase       )
   else:
      _lowerCamelCase   :				Optional[int]							=     ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      _lowerCamelCase   :				List[Any]							=     ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path			,		from_tf=bool(".ckpt" in model_args.model_name_or_path       )			,		config=_lowerCamelCase			,		cache_dir=model_args.cache_dir			,		revision=model_args.model_revision			,		use_auth_token=True if model_args.use_auth_token else None			,		)
   else:
      logger.info("Training new model from scratch"       )
      _lowerCamelCase   :				List[Any]							=     ViTMAEForPreTraining(_lowerCamelCase       )

   if training_args.do_train:
      _lowerCamelCase   :				Any							=     ds['''train'''].column_names
   else:
      _lowerCamelCase   :				Dict							=     ds['''validation'''].column_names

   if data_args.image_column_name is not None:
      _lowerCamelCase   :				Optional[int]							=     data_args.image_column_name
   elif "image" in column_names:
      _lowerCamelCase   :				Tuple							=     '''image'''
   elif "img" in column_names:
      _lowerCamelCase   :				Any							=     '''img'''
   else:
      _lowerCamelCase   :				List[str]							=     column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      _lowerCamelCase   :				Union[str, Any]							=     image_processor.size['''shortest_edge''']
   else:
      _lowerCamelCase   :				int							=     (image_processor.size['''height'''], image_processor.size['''width'''])
   _lowerCamelCase   :				Optional[Any]							=     Compose(
       [
           Lambda(lambda _lowerCamelCase       : img.convert("RGB"       ) if img.mode != "RGB" else img       ),
           RandomResizedCrop(_lowerCamelCase			,		scale=(0.2, 1.0)			,		interpolation=InterpolationMode.BICUBIC       ),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean			,		std=image_processor.image_std       ),
       ]       )

   def preprocess_images(_lowerCamelCase       ):
      _lowerCamelCase   :				List[Any]							=     [transforms(_lowerCamelCase       ) for image in examples[image_column_name]]
      return examples

   if training_args.do_train:
      if "train" not in ds:
         raise ValueError("--do_train requires a train dataset"       )
      if data_args.max_train_samples is not None:
         _lowerCamelCase   :				Tuple							=     ds['''train'''].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
      # Set the training transforms
      ds["train"].set_transform(_lowerCamelCase       )

   if training_args.do_eval:
      if "validation" not in ds:
         raise ValueError("--do_eval requires a validation dataset"       )
      if data_args.max_eval_samples is not None:
         _lowerCamelCase   :				List[Any]							=     (
             ds['''validation'''].shuffle(seed=training_args.seed       ).select(range(data_args.max_eval_samples       )       )
         )
      # Set the validation transforms
      ds["validation"].set_transform(_lowerCamelCase       )

   # Compute absolute learning rate
   _lowerCamelCase   :				Tuple							=     (
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      _lowerCamelCase   :				Tuple							=     training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   _lowerCamelCase   :				Union[str, Any]							=     Trainer(
       model=_lowerCamelCase			,		args=_lowerCamelCase			,		train_dataset=ds["train"] if training_args.do_train else None			,		eval_dataset=ds["validation"] if training_args.do_eval else None			,		tokenizer=_lowerCamelCase			,		data_collator=_lowerCamelCase			,		)

   # Training
   if training_args.do_train:
      _lowerCamelCase   :				int							=     None
      if training_args.resume_from_checkpoint is not None:
         _lowerCamelCase   :				str							=     training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _lowerCamelCase   :				Union[str, Any]							=     last_checkpoint
      _lowerCamelCase   :				Tuple							=     trainer.train(resume_from_checkpoint=_lowerCamelCase       )
      trainer.save_model()
      trainer.log_metrics("train"			,		train_result.metrics       )
      trainer.save_metrics("train"			,		train_result.metrics       )
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      _lowerCamelCase   :				List[Any]							=     trainer.evaluate()
      trainer.log_metrics("eval"			,		_lowerCamelCase       )
      trainer.save_metrics("eval"			,		_lowerCamelCase       )

   # Write model card and (optionally) push to hub
   _lowerCamelCase   :				str							=     {
       '''tasks''': '''masked-auto-encoding''',
       '''dataset''': data_args.dataset_name,
       '''tags''': ['''masked-auto-encoding'''],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**_lowerCamelCase       )
   else:
      trainer.create_model_card(**_lowerCamelCase       )







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Tuple:
   '''simple docstring'''





   main()


if __name__ == "__main__":
  main()







'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


